Probabilistic methods for financial and marketing informatics:
This book offers unique coverage of probabilistic reasoning topics applied to business problems, including marketing, banking, operations management and finance. It shares insights about when and why probabilistic methods can and cannot be used effectively. It offers a complete review of Bayesian ne...
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
San Francisco, Calif.
Kaufmann
2007
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Zusammenfassung: | This book offers unique coverage of probabilistic reasoning topics applied to business problems, including marketing, banking, operations management and finance. It shares insights about when and why probabilistic methods can and cannot be used effectively. It offers a complete review of Bayesian networks and probabilistic methods for those IT professionals new to informatics. Bayesian Networks are a form of probabilistic graphical models used for modelling knowledge in many application areas, from medicine to image processing. They are particularly useful for business applications. |
| Beschreibung: | Literaturverz. S. 397 - 413 |
| Beschreibung: | [X], 413 S. graph. Darst. |
| ISBN: | 0123704774 9780123704771 |
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| 245 | 1 | 0 | |a Probabilistic methods for financial and marketing informatics |c Richard E. Neapolitan ; Xia Jiang |
| 264 | 1 | |a San Francisco, Calif. |b Kaufmann |c 2007 | |
| 300 | |a [X], 413 S. |b graph. Darst. | ||
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| 500 | |a Literaturverz. S. 397 - 413 | ||
| 520 | |a This book offers unique coverage of probabilistic reasoning topics applied to business problems, including marketing, banking, operations management and finance. It shares insights about when and why probabilistic methods can and cannot be used effectively. It offers a complete review of Bayesian networks and probabilistic methods for those IT professionals new to informatics. Bayesian Networks are a form of probabilistic graphical models used for modelling knowledge in many application areas, from medicine to image processing. They are particularly useful for business applications. | ||
| 650 | 4 | |a Bayesian statistical decision theory - Data processing | |
| 650 | 4 | |a Finance - Statistical methods | |
| 650 | 4 | |a Finansiel information | |
| 650 | 4 | |a Information technology | |
| 650 | 4 | |a Markedsanalyser | |
| 650 | 4 | |a Marketing - Statistical methods | |
| 650 | 4 | |a Sandsynlighedsteori | |
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Datensatz im Suchindex
| _version_ | 1804136506518929408 |
|---|---|
| adam_text | Contents
Preface iii
I Bayesian Networks and Decision Analysis 1
1 Probabilistic Informatics 3
1.1 What Is Informatics? 4
1.2 Probabilistic Informatics 6
1.3 Outline of This Book 7
2 Probability and Statistics 9
2.1 Probability Basics 9
2.1.1 Probability Spaces 10
2.1.2 Conditional Probability and Independence 12
2.1.3 Bayes Theorem 15
2.2 Random Variables 16
2.2.1 Probability Distributions of Random Variables 16
2.2.2 Independence of Random Variables 21
2.3 The Meaning of Probability 24
2.3.1 Relative Frequency Approach to Probability 25
2.3.2 Subjective Approach to Probability 28
2.4 Random Variables in Applications 30
2.5 Statistical Concepts 34
2.5.1 Expected Value 34
2.5.2 Variance and Covariance 35
2.5.3 Linear Regression 41
3 Bayesian Networks 53
3.1 What Is a Bayesian Network? 54
3.2 Properties of Bayesian Networks 56
3.2.1 Definition of a Bayesian Network 56
3.2.2 Representation of a Bayesian Network 59
3.3 Causal Networks as Bayesian Networks 63
3.3.1 Causality 63
3.3.2 Causality and the Markov Condition 68
viii CONTENTS
3.3.3 The Markov Condition without Causality 71
3.4 Inference in Bayesian Networks 72
3.4.1 Examples of Inference 73
3.4.2 Inference Algorithms and Packages 75
3.4.3 Inference Using Netica 77
3.5 How Do We Obtain the Probabilities? 78
3.5.1 The Noisy OR Gate Model 79
3.5.2 Methods for Discretizing Continuous Variables* 86
3.6 Entailed Conditional Independencies* 92
3.6.1 Examples of Entailed Conditional Independencies 92
3.6.2 d Separation 95
3.6.3 Faithful and Unfaithful Probability Distributions 99
3.6.4 Markov Blankets and Boundaries 102
4 Learning Bayesian Networks 111
4.1 Parameter Learning 112
4.1.1 Learning a Single Parameter 112
4.1.2 Learning All Parameters in a Bayesian Network 119
4.2 Learning Structure (Model Selection) 126
4.3 Score Based Structure Learning* 127
4.3.1 Learning Structure Using the Bayesian Score 127
4.3.2 Model Averaging 137
4.4 Constraint Based Structure Learning 138
4.4.1 Learning a DAG Faithful to P 138
4.4.2 Learning a DAG in Which P Is Embedded Faithfully* . . 144
4.5 Causal Learning 145
4.5.1 Causal Faithfulness Assumption 145
4.5.2 Causal Embedded Faithfulness Assumption* 148
4.6 Software Packages for Learning 151
4.7 Examples of Learning 153
4.7.1 Learning Bayesian Networks 153
4.7.2 Causal Learning 162
5 Decision Analysis Fundamentals 177
5.1 Decision Trees 178
5.1.1 Simple Examples 178
5.1.2 Solving More Complex Decision Trees 182
5.2 Influence Diagrams 195
5.2.1 Representing with Influence Diagrams 195
5.2.2 Solving Influence Diagrams 202
5.2.3 Techniques for Solving Influence Diagrams* 202
5.2.4 Solving Influence Diagrams Using Netica 207
5.3 Dynamic Networks* 212
5.3.1 Dynamic Bayesian Networks 212
5.3.2 Dynamic Influence Diagrams 219
6 Further Techniques in Decision Analysis 229
6.1 Modeling Risk Preferences 230
6.1.1 The Exponential Utility Function 231
6.1.2 A Decreasing Risk Averse Utility Function 235
6.2 Analyzing Risk Directly 236
6.2.1 Using the Variance to Measure Risk 236
6.2.2 Risk Profiles 238
6.3 Dominance 240
6.3.1 Deterministic Dominance 240
6.3.2 Stochastic Dominance 241
6.3.3 Good Decision versus Good Outcome 243
6.4 Sensitivity Analysis 244
6.4.1 Simple Models 244
6.4.2 A More Detailed Model 250
6.5 Value of Information 254
6.5.1 Expected Value of Perfect Information 255
6.5.2 Expected Value of Imperfect Information 257
6.6 Normative Decision Analysis 259
II Financial Applications 265
7 Investment Science 267
7.1 Basics of Investment Science 267
7.1.1 Interest 267
7.1.2 Net Present Value 270
7.1.3 Stocks 271
7.1.4 Portfolios 276
7.1.5 The Market Portfolio 276
7.1.6 Market Indices 277
7.2 Advanced Topics in Investment Science* 278
7.2.1 Mean Variance Portfolio Theory 278
7.2.2 Market Efficiency and CAPM 285
7.2.3 Factor Models and APT 296
7.2.4 Equity Valuation Models 303
7.3 A Bayesian Network Portfolio Risk Analyzer* 314
7.3.1 Network Structure 315
7.3.2 Network Parameters 317
7.3.3 The Portfolio Value and Adding Evidence 319
8 Modeling Real Options 329
8.1 Solving Real Options Decision Problems 330
8.2 Making a Plan 339
8.3 Sensitivity Analysis 340
9 Venture Capital Decision Making 343
9.1 A Simple VC Decision Model 345
9.2 A Detailed VC Decision Model 347
9.3 Modeling Real Decisions 350
9.A Appendix 352
10 Bankruptcy Prediction 357
10.1 A Bayesian Network for Predicting Bankruptcy 358
10.1.1 Naive Bayesian Networks 358
10.1.2 Constructing the Bankruptcy Prediction Network 358
10.2 Experiments 364
10.2.1 Method 364
10.2.2 Results 366
10.2.3 Discussion 369
III Marketing Applications 371
11 Collaborative Filtering 373
11.1 Memory Based Methods 374
11.2 Model Based Methods 377
11.2.1 Probabilistic Collaborative Filtering 377
11.2.2 A Cluster Model 378
11.2.3 A Bayesian Network Model 379
11.3 Experiments 380
11.3.1 The Data Sets 380
11.3.2 Method 380
11.3.3 Results 382
12 Targeted Advertising 387
12.1 Class Probability Trees 388
12.2 Application to Targeted Advertising 390
12.2.1 Calculating Expected Lift in Profit 390
12.2.2 Identifying Subpopulations with Positive ELPs 392
12.2.3 Experiments 393
Bibliography 397
Index 409
|
| adam_txt |
Contents
Preface iii
I Bayesian Networks and Decision Analysis 1
1 Probabilistic Informatics 3
1.1 What Is Informatics? 4
1.2 Probabilistic Informatics 6
1.3 Outline of This Book 7
2 Probability and Statistics 9
2.1 Probability Basics 9
2.1.1 Probability Spaces 10
2.1.2 Conditional Probability and Independence 12
2.1.3 Bayes' Theorem 15
2.2 Random Variables 16
2.2.1 Probability Distributions of Random Variables 16
2.2.2 Independence of Random Variables 21
2.3 The Meaning of Probability 24
2.3.1 Relative Frequency Approach to Probability 25
2.3.2 Subjective Approach to Probability 28
2.4 Random Variables in Applications 30
2.5 Statistical Concepts 34
2.5.1 Expected Value 34
2.5.2 Variance and Covariance 35
2.5.3 Linear Regression 41
3 Bayesian Networks 53
3.1 What Is a Bayesian Network? 54
3.2 Properties of Bayesian Networks 56
3.2.1 Definition of a Bayesian Network 56
3.2.2 Representation of a Bayesian Network 59
3.3 Causal Networks as Bayesian Networks 63
3.3.1 Causality 63
3.3.2 Causality and the Markov Condition 68
viii CONTENTS
3.3.3 The Markov Condition without Causality 71
3.4 Inference in Bayesian Networks 72
3.4.1 Examples of Inference 73
3.4.2 Inference Algorithms and Packages 75
3.4.3 Inference Using Netica 77
3.5 How Do We Obtain the Probabilities? 78
3.5.1 The Noisy OR Gate Model 79
3.5.2 Methods for Discretizing Continuous Variables* 86
3.6 Entailed Conditional Independencies* 92
3.6.1 Examples of Entailed Conditional Independencies 92
3.6.2 d Separation 95
3.6.3 Faithful and Unfaithful Probability Distributions 99
3.6.4 Markov Blankets and Boundaries 102
4 Learning Bayesian Networks 111
4.1 Parameter Learning 112
4.1.1 Learning a Single Parameter 112
4.1.2 Learning All Parameters in a Bayesian Network 119
4.2 Learning Structure (Model Selection) 126
4.3 Score Based Structure Learning* 127
4.3.1 Learning Structure Using the Bayesian Score 127
4.3.2 Model Averaging 137
4.4 Constraint Based Structure Learning 138
4.4.1 Learning a DAG Faithful to P 138
4.4.2 Learning a DAG in Which P Is Embedded Faithfully* . . 144
4.5 Causal Learning 145
4.5.1 Causal Faithfulness Assumption 145
4.5.2 Causal Embedded Faithfulness Assumption* 148
4.6 Software Packages for Learning 151
4.7 Examples of Learning 153
4.7.1 Learning Bayesian Networks 153
4.7.2 Causal Learning 162
5 Decision Analysis Fundamentals 177
5.1 Decision Trees 178
5.1.1 Simple Examples 178
5.1.2 Solving More Complex Decision Trees 182
5.2 Influence Diagrams 195
5.2.1 Representing with Influence Diagrams 195
5.2.2 Solving Influence Diagrams 202
5.2.3 Techniques for Solving Influence Diagrams* 202
5.2.4 Solving Influence Diagrams Using Netica 207
5.3 Dynamic Networks* 212
5.3.1 Dynamic Bayesian Networks 212
5.3.2 Dynamic Influence Diagrams 219
6 Further Techniques in Decision Analysis 229
6.1 Modeling Risk Preferences 230
6.1.1 The Exponential Utility Function 231
6.1.2 A Decreasing Risk Averse Utility Function 235
6.2 Analyzing Risk Directly 236
6.2.1 Using the Variance to Measure Risk 236
6.2.2 Risk Profiles 238
6.3 Dominance 240
6.3.1 Deterministic Dominance 240
6.3.2 Stochastic Dominance 241
6.3.3 Good Decision versus Good Outcome 243
6.4 Sensitivity Analysis 244
6.4.1 Simple Models 244
6.4.2 A More Detailed Model 250
6.5 Value of Information 254
6.5.1 Expected Value of Perfect Information 255
6.5.2 Expected Value of Imperfect Information 257
6.6 Normative Decision Analysis 259
II Financial Applications 265
7 Investment Science 267
7.1 Basics of Investment Science 267
7.1.1 Interest 267
7.1.2 Net Present Value 270
7.1.3 Stocks 271
7.1.4 Portfolios 276
7.1.5 The Market Portfolio 276
7.1.6 Market Indices 277
7.2 Advanced Topics in Investment Science* 278
7.2.1 Mean Variance Portfolio Theory 278
7.2.2 Market Efficiency and CAPM 285
7.2.3 Factor Models and APT 296
7.2.4 Equity Valuation Models 303
7.3 A Bayesian Network Portfolio Risk Analyzer* 314
7.3.1 Network Structure 315
7.3.2 Network Parameters 317
7.3.3 The Portfolio Value and Adding Evidence 319
8 Modeling Real Options 329
8.1 Solving Real Options Decision Problems 330
8.2 Making a Plan 339
8.3 Sensitivity Analysis 340
9 Venture Capital Decision Making 343
9.1 A Simple VC Decision Model 345
9.2 A Detailed VC Decision Model 347
9.3 Modeling Real Decisions 350
9.A Appendix 352
10 Bankruptcy Prediction 357
10.1 A Bayesian Network for Predicting Bankruptcy 358
10.1.1 Naive Bayesian Networks 358
10.1.2 Constructing the Bankruptcy Prediction Network 358
10.2 Experiments 364
10.2.1 Method 364
10.2.2 Results 366
10.2.3 Discussion 369
III Marketing Applications 371
11 Collaborative Filtering 373
11.1 Memory Based Methods 374
11.2 Model Based Methods 377
11.2.1 Probabilistic Collaborative Filtering 377
11.2.2 A Cluster Model 378
11.2.3 A Bayesian Network Model 379
11.3 Experiments 380
11.3.1 The Data Sets 380
11.3.2 Method 380
11.3.3 Results 382
12 Targeted Advertising 387
12.1 Class Probability Trees 388
12.2 Application to Targeted Advertising 390
12.2.1 Calculating Expected Lift in Profit 390
12.2.2 Identifying Subpopulations with Positive ELPs 392
12.2.3 Experiments 393
Bibliography 397
Index 409 |
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| author | Neapolitan, Richard E. Jiang, Xia |
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| dewey-full | 332.01519542 |
| dewey-hundreds | 300 - Social sciences |
| dewey-ones | 332 - Financial economics |
| dewey-raw | 332.01519542 |
| dewey-search | 332.01519542 |
| dewey-sort | 3332.01519542 |
| dewey-tens | 330 - Economics |
| discipline | Wirtschaftswissenschaften |
| discipline_str_mv | Wirtschaftswissenschaften |
| format | Book |
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| illustrated | Illustrated |
| index_date | 2024-07-02T17:29:50Z |
| indexdate | 2024-07-09T20:57:29Z |
| institution | BVB |
| isbn | 0123704774 9780123704771 |
| language | English |
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| spelling | Neapolitan, Richard E. Verfasser (DE-588)141964383 aut Probabilistic methods for financial and marketing informatics Richard E. Neapolitan ; Xia Jiang San Francisco, Calif. Kaufmann 2007 [X], 413 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Literaturverz. S. 397 - 413 This book offers unique coverage of probabilistic reasoning topics applied to business problems, including marketing, banking, operations management and finance. It shares insights about when and why probabilistic methods can and cannot be used effectively. It offers a complete review of Bayesian networks and probabilistic methods for those IT professionals new to informatics. Bayesian Networks are a form of probabilistic graphical models used for modelling knowledge in many application areas, from medicine to image processing. They are particularly useful for business applications. Bayesian statistical decision theory - Data processing Finance - Statistical methods Finansiel information Information technology Markedsanalyser Marketing - Statistical methods Sandsynlighedsteori Bayes-Verfahren (DE-588)4204326-8 gnd rswk-swf Marketing (DE-588)4037589-4 gnd rswk-swf Kapitalanlage (DE-588)4073213-7 gnd rswk-swf Bayes-Verfahren (DE-588)4204326-8 s Kapitalanlage (DE-588)4073213-7 s Marketing (DE-588)4037589-4 s DE-604 Jiang, Xia Verfasser (DE-588)133055531 aut HBZ Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=015641101&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Neapolitan, Richard E. Jiang, Xia Probabilistic methods for financial and marketing informatics Bayesian statistical decision theory - Data processing Finance - Statistical methods Finansiel information Information technology Markedsanalyser Marketing - Statistical methods Sandsynlighedsteori Bayes-Verfahren (DE-588)4204326-8 gnd Marketing (DE-588)4037589-4 gnd Kapitalanlage (DE-588)4073213-7 gnd |
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| title | Probabilistic methods for financial and marketing informatics |
| title_auth | Probabilistic methods for financial and marketing informatics |
| title_exact_search | Probabilistic methods for financial and marketing informatics |
| title_exact_search_txtP | Probabilistic methods for financial and marketing informatics |
| title_full | Probabilistic methods for financial and marketing informatics Richard E. Neapolitan ; Xia Jiang |
| title_fullStr | Probabilistic methods for financial and marketing informatics Richard E. Neapolitan ; Xia Jiang |
| title_full_unstemmed | Probabilistic methods for financial and marketing informatics Richard E. Neapolitan ; Xia Jiang |
| title_short | Probabilistic methods for financial and marketing informatics |
| title_sort | probabilistic methods for financial and marketing informatics |
| topic | Bayesian statistical decision theory - Data processing Finance - Statistical methods Finansiel information Information technology Markedsanalyser Marketing - Statistical methods Sandsynlighedsteori Bayes-Verfahren (DE-588)4204326-8 gnd Marketing (DE-588)4037589-4 gnd Kapitalanlage (DE-588)4073213-7 gnd |
| topic_facet | Bayesian statistical decision theory - Data processing Finance - Statistical methods Finansiel information Information technology Markedsanalyser Marketing - Statistical methods Sandsynlighedsteori Bayes-Verfahren Marketing Kapitalanlage |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=015641101&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT neapolitanricharde probabilisticmethodsforfinancialandmarketinginformatics AT jiangxia probabilisticmethodsforfinancialandmarketinginformatics |